Borehole imaging tools provide different types of borehole images, for example, electrical tools like the Formation Microlmager (FMI) tool, Resistivity At Bit (RAB) and Azimuthal Laterolog (AzLL) deliver electrical images of the borehole wall, and acoustical tools like the Ultrasonic Imaging Tool (USIT) deliver acoustic images of the borehole wall. Electrical and acoustic borehole images show variations on the borehole wall caused, for example, by geological bed boundaries and fractures. One objective of borehole image processing is to geometrically characterize bed boundaries and fractures. When bed boundaries and fractures are inclined or "dipping" at some angle relative to the axis of the borehole, they sweep out a sinusoidal pattern azimuthally around the borehole image. Each bed boundary or fracture is a "dip event" in the borehole image.
The subject matter of the present invention relates to a workstation and software based method and apparatus for analyzing input data representing images of an internal wall of a borehole, containing a plurality of "dip events" and producing a first plurality of output data comprising a first plurality of "tracks", each of which is a set of connected track points lying along a dip event, the first plurality of tracks representing the respective first plurality of dip events inherent in the input data, and a second plurality of output data comprising a plurality of "dip data" d which are the parameters for a dip event that best fits each first plurality of "tracks" representing the first plurality of "dip events", the software-based method and apparatus of this invention utilizing a "Multi-Sine Tracking" (MST) algorithm derived by modifying a Multi-Target Tracking (MTT) algorithm.
There is a software application, known as "BorView", which receives borehole image data from a downhole borehole imaging tool and which allows an operator sitting at a workstation to manually define, by using a mouse in conjunction with the workstation display, a plurality of "dip events" which are inherent in the borehole image data being displayed on the workstation display. Hereafter, a "dip event" is defined as a formation fracture or a bed boundary or any other type of approximately sinusoidally varying feature which appears in an image of the internal wall of a borehole penetrating an earth formation. In fact, the workstation operator previously had to manually define each such "dip event". However, since there are a multitude of such dip events in the borehole image data, the aforementioned manual operation being performed by the operator is very tedious and can be very time consuming. Consequently, a method and apparatus is needed for automatically defining and generating, as an output, a geometrical characterization and a set of dip data corresponding to all, or nearly all, such dip events in the borehole image data in response to the touch of a key on the workstation keyboard.
In the domain of oilfield data interpretation, there is a problem called "dip estimation" of fitting sine curves to dip events in borehole images. In this specification, an algorithm and associated method known as "Multi-Target Tracking" (MTT) is adapted from outside the oilfield domain. It has the potential to be an extremely efficient means for processing borehole image data in the dip estimation problem mentioned above and disclosed in this specification.
MTT algorithms have been developed, mainly for military applications, over the past 20 years [see the "Bar-Shalom" and "Kurien" references which are cited along with other references in the "reference" section located at the end of this specification]. MTT algorithms combine many different, intermittent sources of information into a self-consistent and complete representation of military and civilian vehicle (e.g. aircraft) identity and location in a space such as a heavily traveled airspace. In military defense applications, MTT algorithms quickly integrate large amounts of data from a diverse set of sensor types (e.g. radar, infrared, imaging sensors, human reports, etc). As described therein, MTT algorithms integrate all available data (prior information, sensor data), with the aid of "models", to form many "hypotheses" about what and where each aircraft or other vehicle is, and how it relates to each data item. One component of MTT, and in particular, the Track Hypotheses Management algorithm, ranks competing hypotheses and ultimately determines which one best represents the data in a manner consistent with the models.
The novelty of the method and associated apparatus disclosed in this specification in accordance with the present invention lies in the recognition that fundamental mathematical similarities exist between the MTT problem and the oilfield dip estimation problem addressed in this specification. The principle challenge of the novel method in accordance with the present invention is to reflect the constraints and characteristics of the oilfield dip estimation problem into the mathematical framework that underlies the MTT algorithm.
The present-day use of the MTT algorithm for military and civilian vehicle surveillance and tracking is reported in the public literature [refer to the "Bar-Shalom" and "Kurien" references in the reference section of this specification]. Although the approach is general, and applies for many different types of sensors and target vehicle types, consider a representative problem of processing radar measurements to track one or more aircraft. As defined in the Kurien reference, the input data consist of a set of "scans". At each time, a scan is a set of measurements generated by the sensor (e.g. a radar scan) from a single look over the entire surveillance volume. Typically, the raw scan measurement data are processed by some type of radar detection pre-processor to create a set of "returns" or "reports" at each time. In the Kurien reference, a "report" is defined to be a set of measurements originating from a single source in a single scan. The output of the detection pre-processor is thus a set of reports, that are the input to the next stage of processing called the Multi-Target Tracking (MTT) algorithm. The functional blocks of the MTT algorithm are shown in FIG. 3.5 of Kurien, reproduced as FIG. 13a of this specification (Kurien assigns the name "Multi-Tracker" to his particular implementation). As shown in FIG. 13a, the MTT algorithm is a global approach to process the input sequence of radar reports from multiple targets, output from the pre-processing Detector to form an output of a set of confirmed tracks, one track per aircraft in the scanned space.
During the MTT processing, a large number of potential "hypotheses" must be considered regarding which of the many input reports is associated with which of the multiple targets being tracked. The algorithmic logic to manage these multiple hypotheses is included in the functional module in FIG. 13a labeled "Track Hypotheses Management". The "Track Hypotheses Management" module itself consists of a collection of functional modules, which are shown in more detail in FIG. 3.6 of Kurien, reproduced as FIG. 13b of this specification. Two of the functional modules within the "Track Hypotheses Management" function shown in FIG. 13b are the "Predict Tracks" and "Update Existing Tracks" modules. These two modules have embedded within them a mathematical physical model describing the way aircraft move and accelerate in space, each model being called a "dynamics model". A representative dynamics model for an aircraft is shown in equation 3.1 of Kurien, rewritten in simpler form here: EQU X.sub.t (k+1)=.THETA.X.sub.t (k)+v.sub.t (k) (A)
where ##EQU1##
In these equations, each target is tracked in a Cartesian frame with the origin located at the sensor position. The target "state" X.sub.t is represented with four variables constituting the position (x, y) and velocity (x, y) of the target in a two-dimensional frame. The variable k represents the discrete time index; .DELTA.T represents the time interval between the discrete time indices k and (k+1); v.sub.t (k) represents a white random Gaussian (noise) process with zero mean; and t represents the target for which the model is applicable (for example, cargo transport planes and fighter jets have different models to represent their differing acceleration capabilites).
In equation (A), it is seen that (1) each component of the target position (x, y) at time (k+1) is equal to that component of the target position at the previous time k plus the incremental change in position obtained by multiplying the corresponding component of target velocity (x, y) at the previous time k times the elapsed time .DELTA.T plus added noise, and (2) each component of the target velocity (x, y) at time (k+1) equal to the corresponding component of the target velocity at the previous time k plus added noise. The vector noise term v.sub.t (k) reflects unpredictable perturbations in the aircraft position and velocity as well as any unmodeled phenomena.
In Borehole Imaging, an electrical or acoustic borehole imaging tool, such as the electrical "Formation Microlmager (FMI)" borehole imaging tool illustrated in FIG. 8, creates a high-resolution "image" of the internal borehole wall. In FIG. 8, the FMI tool creates this high resolution "image" by using an array of small, pad-mounted electrodes [refer to the "Ekstrom" reference in the reference section of this specification]. As the borehole imaging tool is pulled along the axis of the borehole, each pad-mounted electrode acquires a "scan" of data as a function of depth in the borehole. Different electrodes are positioned at different azimuthal positions around the borehole, and the ensemble of scans at many azimuths around the borehole comprises the data to form a borehole "image". As noted in FIG. 11, that image reveals a cylindrical borehole wall that is shown to be unwrapped to span 0 to 360 degrees around the borehole. In FIG. 11, the resolution of the image generated by the FMI tool is on the order of 0.1", revealing fine-scale heterogeneities in the formation.
In FIG. 1, the prominent sinusoidally sweeping features on that "image" correspond to borehole wall "dip events" that are inclined or dipping relative to the borehole axis. One of the most common activities performed when processing borehole images is "dip estimation", which is geometrical characterization of such sinusoidally sweeping features. Identifying the "dip parameters" or sinusoid parameters of the "dip events" corresponds to evaluating the angular inclination and orientation of dipping formation features such as beds and fractures. At a fine scale (centimeters), dip event "fractures" may be recognized in borehole images (see FIG. 11). It should be noted that fractures often cross bed boundaries, and can be highly nonplanar and thus do not correspond precisely to sinusoids in the borehole images; as a result, sophisticated techniques as presented in the subject of this invention are needed for their analysis.
With regard to prior art "Dip Estimation Algorithms", several techniques exist for estimating formation dip parameters using data from borehole imaging and logging instruments. Most of the algorithms use only a subset of the available image data, but some recent algorithms use all of the data provided by borehole imaging tools. Most of today's commercially available algorithms are well established, some having started their development in the 1960's [refer to the Allaud, Chemali, Hepp, Kemp, Moran, Schoonover, and the Vincent references in the reference section of this specification]. They were originally developed before modern wellbore imaging instruments, and were suitable for previous-generation dipmeter tools that acquire only a few channels of data around the borehole wall. Most of these algorithms comprise two steps at each processing depth: (1) a first step where signals acquired along the borehole axis are processed pairwise to estimate curve-to-curve shifts or offsets, for example, by pattern recognition or by finding the peak of a one-dimensional (ID) cross-correlation; and (2) a second step, generally a clustering algorithm, in which all of the offsets from the first step are collectively evaluated to develop an estimate of the formation dip. These methods, although well established in the industry, suffer from several disadvantages, including: (1) they make use of only a small subset of the data available with modern imaging tools; for example, consider the current FMI tool having 8 pads each with 24 buttons for a total of 192 channels of data azimuthally around the wellbore; current dip estimation algorithms typically use only 12 buttons of this data; and (2) they form dip estimates by bulk correlation of segments or zones of data, and thus are not suitable for evaluating individual dipping events such as fractures or individual dipping beds, or in zones where the dip angle varies quickly with depth.
Two more recent classes of dip estimation algorithms make use of the complete image. One class is based on transforms such as the Hough transform that are tuned to find high-contrast events lying along sinusoids in the image [refer to the "Hall" reference in the reference section of this specification], but these have very long computation times, too slow for real-time applications, and do not reliably handle non-sinusoidal events. Another class of algorithm has been developed by Elf [refer to the "Ye" reference in the reference section of this specification] that looks for sinusoidally oriented texture fields; these work well in finely-bedded formations but do not reliably handle isolated, clustered or bed-crossing fracture events.
In a later section of this specification, it is proposed to adapt and extend the Multi-Target Tracking (MTT) algorithm to a novel Multi-Sine Tracking (MST) algorithm, which is utilized in geometrically characterizing fracture, bedding or other approximately sinusoidal dip events in borehole images.
In the problem of borehole image dip estimation, there are potentially a large number of dip events to track and at each processing step a large number of competing hypotheses to handle. MST algorithms adapted from MTT algorithms can manage the combinatorial explosion intrinsic to tracking very large numbers (tens of thousands) of objects. As noted in the "Bar-Shalom" reference cited in the reference section of this specification, these algorithms have the potential to be quite complex because the tracking effort for n targets can be substantially more costly than n times the effort for a single target, because establishing the correspondence between targets and observations is not a trivial matter.
Accordingly, there is a need to utilize, in the oilfield domain, a modified version of the "Multi-Target Tracking" (MTT) algorithms which have been developed for military applications. The modified version of the MTT algorithm, here called the "Multi-Sine Tracking" (MST) algorithm, will be combined with conventional "Detection" and "Best Fit" algorithms to create an overall algorithm called the "Tracking Dip Estimator" (TDE) algorithm, which can be used in the oilfield domain in order to process borehole image data produced by a "borehole imaging tool", that represents images of variations on a borehole wall. Such images come, for example, from a Formation Microlmager (FMI) tool, ultrasonic USIT, azimuthal laterolog resistivity, logging while drilling resistivity at bit (i.e., LWD-RAB), etc. At a single azimuth in the borehole, the borehole imaging tool provides a "scan" of the borehole features along the direction of the borehole axis.
Borehole image processing carried out by the "Detection software" begins by processing the borehole image scan data at each scan azimuth to produce a plurality of image report data, or a set of "reports" at each scan azimuth. Such reports at each azimuthal scan direction are input to the Multi-Sine Tracking (MST) algorithm to recursively produce a plurality of confirmed "tracks", using a procedure adapted from that shown in FIG. 3.5 of Kurien, reproduced as FIG. 13a in this specification. For the MST algorithm as applied to borehole image data, the dynamic model for the target is no longer the model presented in equation 3.1 of Kurien, reproduced as equation A in this specification, but is instead a dynamic model that captures the sinusoidal activity or "dynamics" of borehole image dip events. As a final step of the TDE algorithm, each confirmed track corresponding to each dip event is input to a conventional "Best Fit" algorithm to determine the best fitting "dip data" d which are the parameters corresponding to the dip event that best fits each first output track.